Knowledge-based Framework for Intelligent Emotion Recognition in Spontaneous Speech

AbstractAutomatic speech emotion recognition plays an important role in intelligent human computer interaction. Identifying emotion in natural, day to day, spontaneous conversational speech is difficult because most often the emotion expressed by the speaker are not necessarily as prominent as in acted speech. In this paper, we propose a novel spontaneous speech emotion recognition framework that makes use of the available knowledge. The framework is motivated by the observation that there is significant disagreement amongst human annotators when they annotate spontaneous speech; the disagreement largely reduces when they are provided with additional knowledge related to the conversation. The proposed framework makes use of the contexts (derived from linguistic contents) and the knowledge regarding the time lapse of the spoken utterances in the context of an audio call to reliably recognize the current emotion of the speaker in spontaneous audio conversations. Our experimental results demonstrate that there is a significant improvement in the performance of spontaneous speech emotion recognition using the proposed framework

A system for recognizing human emotions based on speech analysis and facial feature extraction: applications to Human-Robot Interaction

With the advance in Artificial Intelligence, humanoid robots start to interact with ordinary people based on the growing understanding of psychological processes. Accumulating evidences in Human Robot Interaction (HRI) suggest that researches are focusing on making an emotional communication between human and robot for creating a social perception, cognition, desired interaction and sensation. Furthermore, robots need to receive human emotion and optimize their behavior to help and interact with a human being in various environments. The most natural way to recognize basic emotions is extracting sets of features from human speech, facial expression and body gesture. A system for recognition of emotions based on speech analysis and facial features extraction can have interesting applications in Human-Robot Interaction. Thus, the Human-Robot Interaction ontology explains how the knowledge of these fundamental sciences is applied in physics (sound analyses), mathematics (face detection and perception), philosophy theory (behavior) and robotic science context. In this project, we carry out a study to recognize basic emotions (sadness, surprise, happiness, anger, fear and disgust). Also, we propose a methodology and a software program for classification of emotions based on speech analysis and facial features extraction. The speech analysis phase attempted to investigate the appropriateness of using acoustic (pitch value, pitch peak, pitch range, intensity and formant), phonetic (speech rate) properties of emotive speech with the freeware program PRAAT, and consists of generating and analyzing a graph of speech signals. The proposed architecture investigated the appropriateness of analyzing emotive speech with the minimal use of signal processing algorithms. 30 participants to the experiment had to repeat five sentences in English (with durations typically between 0.40 s and 2.5 s) in order to extract data relative to pitch (value, range and peak) and rising-falling intonation. Pitch alignments (peak, value and range) have been evaluated and the results have been compared with intensity and speech rate. The facial feature extraction phase uses the mathematical formulation (B\ue9zier curves) and the geometric analysis of the facial image, based on measurements of a set of Action Units (AUs) for classifying the emotion. The proposed technique consists of three steps: (i) detecting the facial region within the image, (ii) extracting and classifying the facial features, (iii) recognizing the emotion. Then, the new data have been merged with reference data in order to recognize the basic emotion. Finally, we combined the two proposed algorithms (speech analysis and facial expression), in order to design a hybrid technique for emotion recognition. Such technique have been implemented in a software program, which can be employed in Human-Robot Interaction. The efficiency of the methodology was evaluated by experimental tests on 30 individuals (15 female and 15 male, 20 to 48 years old) form different ethnic groups, namely: (i) Ten adult European, (ii) Ten Asian (Middle East) adult and (iii) Ten adult American. Eventually, the proposed technique made possible to recognize the basic emotion in most of the cases

Abstract book : 25th IVR World Congress of Philosophy of Law and Social Philosophy ; law, science, technology ; 15 – 20 August 2011, Frankfurt am Main, Germany

On behalf of myself and my colleagues Professor Dr. Klaus Günther and Professor Dr. Lorenz Schulz, it is my great pleasure to welcome you to the 25th World Congress of the International Association for Philosophy of Law and Social Philosophy (IVR) in Frankfurt am Main. ...Auch im Namen meiner Frankfurter Kollegen Prof. Dr. Klaus Günther und Prof. Dr. Lorenz Schulz möchte ich Sie zu dem 25. Weltkongress der Internationalen Vereinigung für Rechts- und Sozialphilosophie (IVR) in Frankfurt am Main sehr herzlich begrüßen. ..

The influence of incentives and survey design on mail survey response rates for mature consumers

The mail survey is still the preferred research tool for the mature consumer population and questions remain about ways of boosting survey response rates. The influence of two incentives were explored, a foil-wrapped tea bag and a $1 donation for each returned questionnaire in the study design. A significant higher response rate was only achieved for the first incentive. The effectiveness of a range of incentives and survey design features were investigated. Respondents indicated that their preferred incentive was a$500 donation to a charity. With the ongoing use of mail surveys almost mandatory for populations like this one, this study shows that incentives and design features such as CEO endorsement are important elements in improving response rates

Decolonising Public Relations in Africa: Centring Local Epistemes in Ghanaian Political Communication

At independence, African countries were faced with the task of decolonising. Countries were renamed as part of a broader Africanisation agenda. A quest for decoloniality and Africanity was manifest in many ways. However, efforts to decolonise media and communication studies have so far been “media centric”. Outliers such as Public Relations have been left out of urgent debates on decoloniality. This thesis addresses this gap by centring local epistemes in public relations in ways that decolonise political communication, using Ghana as a case study. Despite the growth of democracy in Africa over the last few decades, democratisation in Africa has met with many problems and the role of public relations in Africa’s democratisation is arguably adding to the existing problems. The overarching purpose of this dissertation was to investigate how political parties have utilised decolonised Public Relations (PR), PR strategies, tactics and activities in Ghana’s electioneering campaigns. More importantly, this research was aimed at decolonising Public Relations in Africa by examining the decolonial political PR strategies political parties deploy to win elections. Proverbs instead of global North theories are used to explain what the political parties do in terms of Public Relations. The study employed in-depth interviews and triangulated with a content analysis of media archives. The findings of the research show that while there are opportunities for decolonising PR, the curriculum has to change to reflect the call for decolonised PR. The findings also show that public relations has furthered Ghana’s democracy and ensured that the two main political parties engage voters. Challenges and limitations notwithstanding, the research provides invaluable insights into how African thought and knowledge systems can be applied in public relations, political communication, and its implications for democratisation in Ghana. It contributes original insights to recent debates on decolonisation in African communication and media studies and the subsequent impact of political communication in the African context

Saliva continine levels of babies and mothers living with smoking fathers under different housing types in Hong Kong: a cross-sectional study

Paper Session 15 - The Challenge of Second-Hand Smoke: PA15-3BACKGROUND: After the Smoking Ordinance enacted in HK since 1/2007, shifting of smoking from outdoor to home was found, home becomes a major source of secondhand smoke (SHS) exposure of nonsmokers. OBJECTIVES: It aimed to assess the SHS exposure of babies and mothers living with smoking fathers of two housing types by using a biomarker. METHODS: Trios of smoking father, non-smoking mother and a baby under 18-months were recruited from Maternal and Child Health Centres (MCHCs) from 6/2008 to 10/2009. Consented couples completed the baseline survey including demographic data, fathers’ household smoking behaviors and mothers’ actions in protecting babies from household SHS exposure. Saliva samples from baby and mother were collected and then sent to the National University of Singapore for cotinine analyses. Log-transformations were used for the saliva cotinine due to skewed data. There were 2 housing types (public/private) and father was asked if they smoked at home (yes/no). MANOVA was used to compare the babies’ and mothers’ cotinine levels when fathers smoked at home under the 2 housing types. RESULTS: 1,158 trios were consented. 1,142 mothers’ and 1,058 babies’ samples were assayed. The mean age of the fathers and mothers was 35.5(±7.0) and 31.2(±4.9). The mean mothers’ cotinine level was 12.15ng/ml (±61.20) while babies’ was 2.38ng/ml (±6.01). 606 and 501 trios were living in public and private housing. Fathers’ smoked at home led to higher mothers’ and babies’ saliva cotininary (mean log of mothers’ cotininary: 0.14±0.62 vs. 0.05±0.55, p=0.06; babies: 0.16±0.38 vs. 0.07±0.34, p=0.003). Housing types influenced babies’ cotinine level (public: 0.17±0.37; private: 0.10±0.36, p=0.01). MANOVA showed that fathers smoked at home (Λ=0.99, p=0.01) and housing types (Λ=0.99, p=0.01) were positively related to the saliva cotinine levels. CONCLUSIONS: Father smoked at home and the housing types have greater impact on babies’ saliva cotininary, showing that they were highly exposed at home and in public housing environment. HK government should promote smoke-free homes and to provide more smoking cessation services to minimize the household SHS exposure to babiespublished_or_final_versio

Salient Features for Anger Recognition in German and English IVR Portals

Anger recognition in speech dialogue systems can help to enhance human commputer interaction. In this chapter we report on the setup and performance opti-izationtechniques for successful anger classification using acoustic cues. We evaluate the performance of a broad variety of features on both a German and an American English voice portal database which contain “real” (i.e. non-acted) continuous speech of narrow-band quality. Starting with a large-scale feature extraction, we determine optimal sets of feature combinations for each language, by applying an Information-Gain based ranking scheme. Analyzing the ranking we notice that a large proportion of the most promising features for both databases are derived from MFCC and loudness. In contrast to this similarity also pitch features proved importance for the English database. We further calculate classification scores for our setups using discriminative training and Support-Vector Machine classification. The developed systems show that anger</p